# search.py
# ---------
# Licensing Information:  You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
# 
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).


"""
In search.py, you will implement generic search algorithms which are called by
Pacman agents (in searchAgents.py).
"""

import util


class SearchProblem:
    """
    This class outlines the structure of a search problem, but doesn't implement
    any of the methods (in object-oriented terminology: an abstract class).

    You do not need to change anything in this class, ever.
    """

    def getStartState(self):
        """
        Returns the start state for the search problem.
        """
        util.raiseNotDefined()

    def isGoalState(self, state):
        """
          state: Search state

        Returns True if and only if the state is a valid goal state.
        """
        util.raiseNotDefined()

    def getSuccessors(self, state):
        """
          state: Search state

        For a given state, this should return a list of triples, (successor,
        action, stepCost), where 'successor' is a successor to the current
        state, 'action' is the action required to get there, and 'stepCost' is
        the incremental cost of expanding to that successor.
        """
        util.raiseNotDefined()

    def getCostOfActions(self, actions):
        """
         actions: A list of actions to take

        This method returns the total cost of a particular sequence of actions.
        The sequence must be composed of legal moves.
        """
        util.raiseNotDefined()


def tinyMazeSearch(problem):
    """
    Returns a sequence of moves that solves tinyMaze.  For any other maze, the
    sequence of moves will be incorrect, so only use this for tinyMaze.
    """
    from game import Directions
    s = Directions.SOUTH
    w = Directions.WEST
    return [s, s, w, s, w, w, s, w]


def BasicSearchFragme(open_list, problem: SearchProblem):
    """
    A basic search frame.Inherited by the other search algorithms with different open lists types.
    :param open_list: open list (sorting category defined before)
        each item in open_list is a list, containing current state and the searched path list (for actions)
    :param problem: SearchProblem
    :return: searched path to goal containing actions
    """
    close_list = []
    open_list.push([problem.getStartState(), []])
    while not open_list.isEmpty():
        # get current search node
        nextNode= open_list.pop()
        nextState = nextNode[0]
        if problem.isGoalState(nextState):
            # return a searched path to goal containing actions
            return nextNode[1]
        if nextState not in close_list:
            close_list.append(nextState)
            # expand the search tree
            for successor in problem.getSuccessors(nextState):
                if successor[0] not in close_list:
                    # add new action to the current search path
                    new_path = nextNode[1][:]
                    new_path.append(successor[1])
                    open_list.push([successor[0], new_path])
    return


def depthFirstSearch(problem):
    """
    Search the deepest nodes in the search tree first.

    Your search algorithm needs to return a list of actions that reaches the
    goal. Make sure to implement a graph search algorithm.

    To get started, you might want to try some of these simple commands to
    understand the search problem that is being passed in:

    print("Start:", problem.getStartState())
    print("Is the start a goal?", problem.isGoalState(problem.getStartState()))
    print("Start's successors:", problem.getSuccessors(problem.getStartState()))
    """
    # stack for openl list in DFS
    open_list = util.Stack()
    return BasicSearchFragme(open_list, problem)


def breadthFirstSearch(problem):
    """Search the shallowest nodes in the search tree first."""
    # queue for open list in BFS
    open_list = util.Queue()
    return BasicSearchFragme(open_list, problem)


def uniformCostSearch(problem):
    """Search the node of least total cost first."""
    # function to count the searched path cost
    def pathCost(item):
        return problem.getCostOfActions(item[1])

    # min-heap sorted by path cost for open list in uniform search
    open_list = util.PriorityQueueWithFunction(pathCost)
    return BasicSearchFragme(open_list, problem)


def nullHeuristic(state, problem=None):
    """
    A heuristic function estimates the cost from the current state to the nearest
    goal in the provided SearchProblem.  This heuristic is trivial.
    """
    return 0


def aStarSearch(problem, heuristic=nullHeuristic):
    """Search the node that has the lowest combined cost and heuristic first."""
    # function to count the searched path cost and heuristic cost
    def heuristicPathCost(item):
        return problem.getCostOfActions(item[1]) + heuristic(item[0], problem)

    # min-heap sorted by path cost and heuristic cost for open list in A star search
    open_list = util.PriorityQueueWithFunction(heuristicPathCost)
    return BasicSearchFragme(open_list, problem)

"""
item[0]:state
item[1]:path(actions)
"""


# Abbreviations
bfs = breadthFirstSearch
dfs = depthFirstSearch
astar = aStarSearch
ucs = uniformCostSearch
